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. 2018 May 8:6:2100510.
doi: 10.1109/JTEHM.2018.2822681. eCollection 2018.

An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning

Geng Yang  1 , Jia Deng  1 , Gaoyang Pang  1 , Hao Zhang  1 , Jiayi Li  1 , Bin Deng  1 , Zhibo Pang  2 , Juan Xu  3 , Mingzhe Jiang  4 , Pasi Liljeberg  4 , Haibo Xie  1 , Huayong Yang  1
Affiliations

An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning

Geng Yang et al. IEEE J Transl Eng Health Med. .

Abstract

Surface electromyography signal plays an important role in hand function recovery training. In this paper, an IoT-enabled stroke rehabilitation system was introduced which was based on a smart wearable armband (SWA), machine learning (ML) algorithms, and a 3-D printed dexterous robot hand. User comfort is one of the key issues which should be addressed for wearable devices. The SWA was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. By evenly distributing surface electrodes over user's forearm, drawbacks of classification accuracy poor performance can be mitigated. A new method was put forward to find the optimal feature set. ML algorithms were leveraged to analyze and discriminate features of different hand movements, and their performances were appraised by classification complexity estimating algorithms and principal components analysis. According to the verification results, all nine gestures can be successfully identified with an average accuracy up to 96.20%. In addition, a 3-D printed five-finger robot hand was implemented for hand rehabilitation training purpose. Correspondingly, user's hand movement intentions were extracted and converted into a series of commands which were used to drive motors assembled inside the dexterous robot hand. As a result, the dexterous robot hand can mimic the user's gesture in a real-time manner, which shows the proposed system can be used as a training tool to facilitate rehabilitation process for the patients after stroke.

Keywords: IoT-enabled wearable device; machine learning; sEMG control; stroke rehabilitation.

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Figures

FIGURE 1.
FIGURE 1.
Architecture of IoT-enabled stroke rehabilitation system.
FIGURE 2.
FIGURE 2.
Smart wearable armband (SWA).
FIGURE 3.
FIGURE 3.
Target gesture set: a) AG, b) CH, c) OH, d) PT, e) T&M, f) T&L, g) FH, h) EH, i) RE.
FIGURE 4.
FIGURE 4.
Offline classification accuracy using individual feature.
FIGURE 5.
FIGURE 5.
Offline classification accuracy using different combinations of features.
FIGURE 6.
FIGURE 6.
Data analysis tool using CCEAs . Section 1 illustrates the distance of each class and its closest neighbors in feature space (2D). Section 2 shows a table in the order of the number of times that a movement is the most conflicting neighbor for any of the other movements. Data from a recording session of nine movements was showed. The feature extraction setting is “tmabs”, “tstd”, “twl”, “trms”, and “tvar”. Section 3 shows a table of movement conflicts.
FIGURE 7.
FIGURE 7.
The distribution of nine classes in feature space (3D) leveraging PCA: (a) the feature extraction setting is “tzc”; (b) the feature extraction setting is “tmabs”, “tstd”, “twl”, “trms”, and “tvar”.
FIGURE 8.
FIGURE 8.
Offline classification accuracy and training/validation time using different classifiers.
FIGURE 9.
FIGURE 9.
Offline and real time classification accuracy of nine movements.
FIGURE 10.
FIGURE 10.
Real time gesture recognition results to control a five-finger dexterous robot.
See this image and copyright information in PMC

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